00:00:00.00 [Announcer]: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:14.88 [Michael Helbling]: Hey, everybody, welcome. It’s the Analytics Power Hour, and this is episode 305. Follow your passion and you will never work a day in your life. We all know that quote. We also all know it’s kind of bullshit. But sometimes working hard and combining your passions, I think it can work out well. You know, as with any career in data, oftentimes how it is applied and to what is maybe kind of the real thing that energizes you. And I guess we wanted in this episode to have a bit of fun and talk to someone who has combined their data skills and their passion for something into a unique and wonderful career. But first, let me introduce my co-hosts. Julie Hoyer, how are you today?

00:00:58.70 [Julie Hoyer]: Hey there. I’m doing great.

00:01:01.16 [Michael Helbling]: Excellent. Great to have you. And Tim Wilson, how are you? Doing well. A little over caffeinated. Tim is a great example of someone turned his data skills into his passion of telling people that they’re wrong. So that’s amazing. Wow. And I’m Michael Helbling. All right. Our guest today is someone we are excited to talk to. Stephen Follows. He is a film data researcher whose mission is to help filmmakers get their films funded, shot and seen.

00:01:28.02 [Michael Helbling]: He’s a film data researcher. He consults with the Guinness World Records on film-related world records. He writes and researches extensively on the film industry at StephenFollows.com. And he’s also the founder of CatSnake, a storytelling agency that helps nonprofits identify and leverage narratives. And today he is our guest. Welcome to the show, Stephen.

00:01:46.21 [Stephen Follows]: Thank you so much for having me. And it’s really fun to be the fun guest rather than in the film industry where I’m the boring analytics guy. So that’s quite nice.

00:01:54.07 [Julie Hoyer]: Oh, yeah. That’s true. In this context, you’re kind of the cool… I never thought about that juxtaposition. That’s incredible.

00:02:04.68 [Michael Helbling]: Yeah. You’re at home. You’re with friends. Yes, right. Thank you so much. I’ve needed this so much. I’m so grateful. So, yeah. So to jump into it, I think, you know, obviously you have an incredible website where you do tons of research and articles and things like that. But I think the first question that came to my mind is sort of, yeah, how did this all begin? Like, how did you kind of combine these two things? So which came first? Was it a love of film or was it kind of a love of data? Like, what was the genesis of this?

00:02:34.97 [Stephen Follows]: Well, I get asked that a lot, I think, because what I do is unusual. Or at least people think they understand one part of it and then they hear about the other half. And that doesn’t make any sense in that sort of image they have. It makes sense to me, but I think I’m the only one. So when I was a teenager, I didn’t know what I wanted to do. I knew I had aptitude in some things, but I also knew that I wasn’t just going to follow along a path. And so I knew I wanted to do. I wanted to do film and I wanted to do thinking. They were the two things. And that’s only the extent to which I knew what I wanted to do. And so everybody said, go to university and study thinking. Go do philosophy or there’s analytics, something like that. And then just do film on the side. And because I’m pathological demand avoidance, I did the opposite. And I went into an art college and did a three-year degree in film. And never having done any film, never filmed anything before. And then that led on a path where I made, after that, I set up a little production company. We made short films. And we made films for charities and TV habits. And that sort of went on a journey. But as that was going on, I knew that I wasn’t doing the thinking bit. And I could see that film wasn’t going to take me to that place where I get to use those particular parts of the brain I wanted to use. So I started doing it off my own back, doing analytics for friends. They were putting a business plan together. And I mean, it was a pretty bad year because everyone said to me, I am making this film. I know it’s going to make a lot of money. Can you prove it? I was like, great, I’ll go and do that. And then I could never prove it would make money. And then after a year, I went, oh, none of them make money. I thought I was doing it wrong. And it turns out, you know, it’s passion. It’s all fine. So I ended up doing that. And I started sharing it online. And then, as you guys know, like whenever you have a passion or a niche thing and then you do it again and again, you become like a lighthouse. And people come to you and you build a community or you take part in existing communities. And that kind of grew from there. So for the last 15 years or so, I’ve been publishing two or three times a week with all sorts of different data studies, trying to look at film in different lenses. And that’s led on to various things. So these two paths of a company that was a film company now raises money for charities with film and story. And on the other hand, being the analytical view on the film industry makes sense to me is two sides of me. But they sort of there aren’t many people that would do both.

00:04:46.28 [Tim Wilson]: I think in those early days, how like what portion of the if you just look like the raw time and thought work you were doing, how much were you doing that? Like didn’t pay or didn’t have like a direct I’m getting compensated for this. Like were you was it clear that you’re like, well, some of the stuff I’m doing is a passion and hopefully it’ll turn into something. And the other stuff I’m doing to pay the bills. Like, what did that look like?

00:05:11.34 [Stephen Follows]: Well, that was easy because nothing paid me anything. So that’s an easy question, right? So I think I’m used to following passions that then might pay off later on. So it’s not that I was privileged or had the ability to do that. It’s that when I left. University, although I said I was doing my own production company, I was at a full time job working in a sort of marketing video company as a runner. Actually, I applied to be a runner. The lowest job didn’t get the job. They then the person they hired concluded that it was too much work for them and they hired me to be their assistant. So there was no title for it. I just did everything. So that was a year and a half or so where I was earning money from one place and then trying to build up my own agency. And then I started doing them. I moved to a tiny, tiny. Little office in a film studio. And that was right next to a film school. And I like teaching. I like sharing things. And so I started doing guest lecturing and then that grew. And then in the evenings and weekends, I was teaching there trying to set my agency up. So my agency wasn’t paying me very much. I think my business partner reminded me recently the first month we got paid 86 pounds each for the month. And so like that never made any money. But I was teaching. And then as the agency started to make money, I could do less and less teaching. But I was then at that point also doing the writing. And the analytics stuff. And it’s only occasionally I get paid to go like an honorarium to do a talk somewhere. But mostly it was about the travel being fun. And then I started to be able to turn it into money. But I only turned what was a WordPress blog entirely for free into a sub stack with a paid component in the last year and a half. I could have done it earlier, but I didn’t get around to it. And so, yeah, I think the money has always followed way, way later than the work. And I kind of expect. That now, like I just I don’t start anything thinking, well, there’s pay because it probably won’t. And it certainly won’t now. Got to start as a passion project. I don’t know if this is good advice. I only know this is what I did. Please don’t anyone follow this.

00:07:10.13 [Julie Hoyer]: But yeah, it sounds right to me. I was going to ask, too, with that initial question that you had, like your friends asking you, like, will this film make money? And you started to actually dig into that. Where did you start? I mean, that is such a big, broad question to answer. Where did you start if you didn’t do schooling and like the thinking side of what you wanted to do? Like you were saying, like, what were your first steps to attack that question and what resources did you use? What what did you try?

00:07:38.48 [Stephen Follows]: Yeah, I mean, so it’s actually fun to think back because it’s so different nowadays. But it’s a great question. So I always had an aptitude for maths, but no interest in it in and of itself. So I started it to 16 GCSE is the is the is the sort of first set of compulsory exams in the UK when I was a kid. And so I did maths. I did quite well. I think I forgot to bring a calculator into one of the exams and they still got an A. So I had an aptitude, but I had no interest because I forgot to bring a calculator. I just didn’t care. And I remember saying, can I have a calculator?

00:08:06.74 [Tim Wilson]: You were Julie’s kindred spirit. This is.

00:08:08.54 [Stephen Follows]: I just didn’t care. It just no one could convince me it was meaningful. And even now, actually, I still don’t think the maths part is I mean, we all have we have AIs. We don’t like I’m not saying that the maths doesn’t matter. I’m saying that it’s only a part of a larger point around logic or. And so it was all first principles. Yeah. So I didn’t go in there thinking, oh, I know how films make money. I mean, I have no idea. So I went around and spoke to people. And it’s not that you can get people’s private financial data or anything like that. You’re not getting true insight. But what you can do is say, well, we know these ones were very successful and these are the numbers we do have. And back then you’d have video numbers and you’d certainly have theatrical theatre, cinema money. And you could say, OK, well, when I film on this budget makes at least this much and then, you know, it’s probably in the profitable end. So. What we’re trying to prove here is not that it’s a good business investment, because I can assure you, no film is a good business investment. We’re trying to prove that they’ve done the best they can, given that it’s not a financial thing. It’s a passion project or they’re trying to convince an investor who’s going to get big tax breaks for doing it to do this one, not that one. All you’re trying to do is show that you’ve done more thinking than everyone else and that you’re more likely to be in that category of things that might break out. So the bar is very, very low. I’m not having to do a proper business plan. I’m showing that they understand the market. That they are favorably comparable to similar ones. And also that you do every now and then get a Blair Witch film, a Blair Witch project or a paranormal activity that can make 100 times its budget. And then they sell the investment in their film as a kind of like this is we fund, but it’s a lottery ticket. But when it does happen. So it’s it was very loose. I didn’t have to understand business school economics. I had to understand what numbers were there and how this film compares with all those films. I have numbers for.

00:09:56.90 [Julie Hoyer]: Mm hmm. So then as you continue to do these different analyses and writing and things, what have you focused on then on the thinking side? Is it more of that like psychology? Is it more classic business stuff? Is it heavier, actual like mathematics?

00:10:15.78 [Stephen Follows]: I mean, it’s all of that stuff. I mean, I never really learned to code. I learned PHP a little bit when I was at university, but self-taught, but I didn’t really use it. Excel was my main driver for a long time. And I used to get teased from a lot of scientific science friends, quite rightly, that I was only using that and it was very slow to do anything else. And so I but I would talk to people and I would try and just I don’t mind saying I don’t know. And I’ll go and ask. And after a while, you realize nobody knows or there are one or two things that you that are behind some sort of barrier. And if you chat to people, you can get that information so I can learn rules of thumb and apply them to data we do see. Because although I didn’t have any. Secret access or training. No one else has special training for this. This isn’t like being a doctor or a lawyer where there is a corpus of facts that you do need to know. And if you get wrong, you know, there’ll be consequences. This is a lot more subjective because it’s the arts and it’s the business of art rather than just business. So those people had to learn them somehow. And I didn’t have the resources or the data or their personal data. They’re the films they worked on. But people would show me stuff or tell me stuff. And especially when I started blogging. It people would come back. I’d never share anything private, never getting any trouble. But I would say some, you know, I’d love to know about this. And someone would say, hey, come have a coffee with me. And they’d show me stuff. And I’d be like, oh, I understand how that works now. So when I go on the next project, I can apply the same logic and know that it’s broadly right. And then somebody would correct me if I got something wrong. And so it was sort of just asking and doing and trying to be humble and curious, I guess.

00:11:53.90 [Tim Wilson]: Michael. How many times? Have you rebuilt the same analysis? Oh, that depends. Are we counting the times I said this time I’m documenting it? Absolutely. Then many, many times. That’s where super skills in Prism come in. You can take an entire analysis, including multiple steps, dependencies, conditions and checks, and package the whole thing into a reusable template.

00:12:17.13 [Michael Helbling]: Oh, so not just one query, the whole ridiculous journey?

00:12:20.50 [Tim Wilson]: The whole ridiculous journey. Run it again on new data or a completely different.

00:12:44.46 [Tim Wilson]: and they will move you to the top of the list.

00:12:54.08 [Michael Helbling]: Reuse the analysis and save the artisanal suffering. Tim, how many browser tabs does it take to answer one simple measurement question?

00:13:03.80 [Tim Wilson]: Apparently, all of them. STAPE, GTM, GA4, and at least three tabs I’m afraid to close.

00:13:10.51 [Michael Helbling]: Yeah, I’ve lived that life. STAPE’s AI Assistant is designed to help with exactly that. It’s built directly into STAPE and can connect to Google Tag Manager and GA4.

00:13:21.24 [Tim Wilson]: So you can ask questions about your containers, review GTM configurations, check server-side container usage, or see whether new events are actually showing up in GA4.

00:13:31.18 [Michael Helbling]: Yeah, and it can do more than answer questions. You can ask it to create STAPE and server-side GTM containers or even set up tags and triggers in GTM.

00:13:39.84 [Tim Wilson]: Basically, fewer scavenger hunts through menus.

00:13:42.70 [Michael Helbling]: Yeah, exactly. It is conversational, built into STAPE, and there’s no separate MCP server or technical installation required. And I mean, honestly. The best part, you’re not burning tokens

00:13:53.74 [Tim Wilson]: from your own LLM subscription. If you want to see what STAPE’s AI Assistant can do, click the link in the show description to learn more. Presumably, there’s a lot more data now. And at some point, we’ve got to talk about the numbers, because it’s just timely, because that’s kind of wild what’s going on there. But when you started, was there a point where you said, I need to start formally capturing or cataloging either what I’m getting one-on-one?

00:14:21.08 [Unknown]:

00:14:21.08 [Tim Wilson]: Anecdotally, versus what the data sources are? Did you come across a year or two in, and you’re like, oh my god, I just found a gold mine? Where did, when did you start structuring it? Because it sounds like you sort of became the guy who had kind of a unique level of breadth and depth in one angle of the industry. But that also seems like, how did you keep that organized? Did you find yourself kicking yourself three years? And then saying, oh, I wish I had done something more structured? Like, does that make sense?

00:14:56.37 [Stephen Follows]: Yeah, no, totally. And it is a little bit about how you create stuff yourself. I’d breach, I was led by articles back then. So I’d be like, OK, I got to write an article. I want to do one. Back then, I was doing one a week. So I’d create a folder for it. And you start creating, OK, there’s a data folder. And there’s an images folder for later on. And then I would create, you’d get, grab, gather unique data sets for each one. But after a while, you start realizing, oh, no, I can use that data set again. And I’d organized all of this. And I showed it to somebody after a year. Or two of doing it. And they’re like, wow, this is really organized. And I remember thinking, no, it’s not. It’s pretty basic. And I think that people who think they’re messy probably have a quite tidy house because their bar is quite high. So my bar of how to do this if I was paid or what I would tell someone else to do is a lot higher than what I was doing. But because I was in a field where no one’s doing that, no one runs away to do the maths for the circus. So I was one of the very few people doing this. And in fact, actually, I gave a talk at a business school early on. And someone came up to me afterwards and said, oh, you must. That book, I can’t remember how they mentioned it. But they mentioned it in passing that a certain book had obviously been very important to me. And it was a book called Category of One. And I was like, I’ve never read this. What is this? And they were like, really? And so I went away and read it. And it’s a very good old marketing book about the idea that one of the things you can do in marketing is create a new category, like light beer. And then you’re number one in that category. And he had concluded from my work that I had been decided to be the only one. I was the only guy doing data in the film industry. So therefore, I would be the best one, which is mathematically true. But a lot of this is I was the only one doing it. And in answer to the other half of the question, which is about like, so I would go out and gather data. Initially, I think the first article I wrote that really took off after three or four articles were doing it was I went through someone else’s work. I went through the British Film Institute, the public body. And they had done a huge statistical yearbook, which I was reading every year for fun anyway. And it was already kind of fun and well-written. It’s helping for my education and also for teaching other people. And I’d written 50 interesting things we’d learned. It was like an article like that. And then lots of people appreciate that. So there were bodies like that who were already sharing data. And then you have private bodies like IMDB and people like that who’ve done a lot of the work cataloging it, either as an organization or as a collection of people like Wikipedia. And a big step forward for me in the very early days was learning how web scraping worked so that I could go and take all of Wikipedia, put it into Excel,

00:17:20.29 [Unknown]:

00:17:20.29 [Stephen Follows]: And then I could go and do it. And then I could go and do it. And then I could go and do it. And then I could go and do it. And then be able to do everything myself. Because again, I wasn’t coding very much. So a big breakthrough was web scraping. I would talk to a friend who’d do it, or there’d be a tool off the rack. Or sometimes I used People Per Hour quite a lot where I knew what I was doing was way out of my capability, but it was also incredibly basic for anyone who knew what they were doing. So I was happy to give someone a few bucks, really a few bucks, like not very much. I might be like, hey, I put a post out saying, hey, I know, I believe this to be very simple because someone did it for me before and it’s just tweaking this. How much do you, what does someone want for doing this? And they’d look at it and be like, oh, give me 20 bucks. Or they’d build me a self-contained tool as a one-off use for 50 bucks. That wasn’t every week, but every now and then that would give me a large data set because I could run that, gather all this data, which wasn’t always taking data off other people’s things. It wasn’t that I was taking IMDB’s database, but it might be that I was taking reviews or other things that I could turn into data. And then the only thing I was ever sharing or publishing was my thoughts and the, the combined graphs at the end. So that data, I didn’t do anything with it. I didn’t give it away, sell it, all that. So I felt quite ethical that it was, although I wasn’t part of an academic institution formally, I was doing that the way a university might. And so I never got in trouble from that really. And from that, I was able to say, right, I went to this site and this site and this site, and I combined all those insights, and this is what I found. And here’s three graphs that are interesting. See you next week. And that seemed to be quite sustainable for the first few years.

00:18:47.44 [Tim Wilson]: It’s funny, because when you talked about like the ones, I feel like that once a week discipline, when I started blogging 2007, it was literally just a, I will do something once a week. And there was some nerves upfront of like, what happens after three or four weeks? Will I be out of things to say? Which I never was. I certainly watch, and this happens with podcasts and blogs and substacks now where somebody, they’re like, I’m gonna start doing this. And the first thing they write is, they’re gonna write this. They’re gonna write this.

00:19:20.36 [Unknown]:

00:19:20.36 [Tim Wilson]: They’re gonna write this. And then they write this like half of a book. Like they just empty their soul into it. And then the second thing is like a week later than they expected. And the third one never comes. So it does feel like you had that. I think even what I was doing, there was a lot, I had a lot of passion for like little tips and tricks in Excel and was never like the Excel God would never win any sort of Excel competition. But I was like, hey, I found this thing and it took some digging. But it seems like you had that mix of, I’m gonna hold myself to this discipline. It is something I care about. Was there a component of it? And instead of just having idle thoughts or exploring it, if I tell myself I’m gonna write it down by this date and explain it clearly, that will help with my thinking as well? Like you were consciously like building up your mind. Was there any sort of muscle on that front?

00:20:22.37 [Stephen Follows]: I think, I mean, I’ve definitely done my fair share of exactly as you said, half a novel a week late and then gone. And I’ve done a fair number of them. I don’t like that. I feel very bad about that. When I start a new project, I really want to avoid that. I think I want things to be interesting to me and I’m naturally a curious person. So I found the whole process of doing it was primarily for my own knowledge. I wanted to learn all of this and I would, the film industry, although it is a niche, I guess, within it is huge numbers of things to look at, you know, on screen, off screen, behind the scenes, all these different jobs, money. So every week I’d pick something and I deliberately would find something as far away as I could. So if I was looking at money one week, I would think about actors the next week. And so I enjoyed reading my blog slowly the few days before everyone else. And then the teacher in me, I always want to, I want to work out how things work and share it with other people. Like that is a deep pleasure of mine. What they then do with it, it’s up to them. Like I never thought like they should, I should have a hand on the teller of what they’re making. But I wanted to share this information. So the course of the week, so I’d publish on Monday morning. So the course of the week would be that I’d be thinking about it during the week and maybe I’d spend a bit of time gathering some data or I had something, but I would think about it. And then the deadline would be coming up and I’d be like, oh shit, I really should catch up. And so, or maybe on the weekend, I would do some work on it and get the, oh, all right, I got the thing now. I know what the thing’s about. And then it would be writing up on the Sunday or rushing it on the Sunday. And I quite enjoyed that Sunday morning, kind of like, okay, so I sort of have to do a summary of everything I learned this week about this. And I started to establish the question as the headline format that I then talk about the answer, but talk about how I went about, found the answer in a way that everyone feels like, oh, I didn’t know that, that makes sense. Oh, that’s good to know. And so I would enjoy that process of almost tidying up on a Sunday, of tidying up those thoughts and presenting them, queuing it up, and then that’s ready to go on Monday. So I found it useful, although it wasn’t the driver. It wasn’t that I was like, I’ve seen people that have calendars that want to have like a no broke, no, you know, a streak that’s not broken. It wasn’t that, it was just when else was I going to force myself to sit and organize those thoughts and actually write something up. This podcast does have an unbroken streak of,

00:22:32.64 [Tim Wilson]: we have one co-host who might have that motivation unhealthily so.

00:22:37.55 [Stephen Follows]: My business partner in my other business, which maybe we’ll talk about it first time, has got OCD. And I think that has been one of the best business decisions I’ve ever made because we are never late on anything, otherwise his world ends. And I have, like I want depth and accuracy and things like that, which he cares about, but only through me. So that can be such a good self-sustaining, team building thing, as well as a good place to put those thoughts if you have them. So yeah, I think good on you. That’s really good.

00:23:05.42 [Tim Wilson]: Clearly we got the wrong person on this episode because that’s the guy I want to talk to.

00:23:12.51 [Julie Hoyer]: How do you catalog and then like pick what question you’re going to go for each week? I know you said like you, I liked that you were saying you try to go for something as far away from the previous week. And it’s amazing just that the film industry does have so many different like angles and lenses you can kind of go after. But I mean, how do you keep track of what sparks like interest for you? And then how do you decide, is there like an official catalog? What do you use to prioritize? I am awful at that.

00:23:44.33 [Stephen Follows]: And in the first few years, I know maybe the middle few years before the pandemic, but after I got into a rhythm of this, I had to remind myself every time that I was about to do something to Google it first to find out if I’d already done it. Because like, I just wouldn’t quite remember. And quite often like the investigation is quite large, but the article you write has to be quite narrow and focused. And so you write one part of it up, but you know it’s sort of unfinished in your head or what was the interesting part for everyone else is actually not the bit that interests you. So you can write something, publish it and forget all about it. And so I have an image of it. And in more recent years, including last year, I forgot that rule and published an article a week after I published it, realized it was basically the same article I’d done six months before. Oh. About like, I think it’s about like when bad films make money or like what are the conditions under which some films that critics don’t like, why they still make money. And I wrote the question in two different ways. And like, I left them both up there because it doesn’t matter. But it’s, and I was quite relieved to find the same answer. And maybe people wouldn’t realize, but, you know, I was like, oh, I actually did this. So I did, when I have a random idea, I did have a Dropbox paper document, or I guess I still do somewhere. And I write it in there. Or if I had like a data set, I’d put it in a separate set of folders than the actual articles. And now, thanks to things like AI and things like that, I’m able to do things at much greater speed when it comes to the analytical part, not the writing, because that was still a human. So that’s the bottleneck. So I now have a work in progress folder that has about 250 projects done, but not written up. Wow. And so that’s, that’s the bottleneck.

00:25:17.86 [Unknown]:

00:25:17.86 [Stephen Follows]: So that’s the bottleneck.

00:25:17.94 [Unknown]:

00:25:17.94 [Stephen Follows]: So that is a challenge, but I also believe in the evolution of ideas. If it’s a good enough idea, it’ll come back again, or I’ll be compelled to do it. So it’s, because I have no boss, and it doesn’t matter that I forgot something, I kind of don’t mind. I wouldn’t suggest any, this is all terrible advice. But for me, it didn’t matter if I had a great idea and dropped it, because it would come back again if it was a great idea. And if it didn’t, that’s fine. But as I said, I don’t think, that’s not a professional way of doing it, if you’re a business. Who needs to work through a set list.

00:25:49.81 [Julie Hoyer]: Yeah. But I think it just speaks to the fact that, like you said, this is your passion. You’re able to do it in your way of working, and it’s worked because nobody else is doing it. And so it doesn’t really matter if you have a perfect system to prioritize in this setting. I am curious though, because you were saying, you know, the help of AI. I had seen a title when I was looking at some of your articles and it was about movie poster colors analysis that you did. And I immediately was blown away. I was blown away by like, how did you get that data? Like, how did you go out? I mean, you, I don’t have the exact number, but it was tens of thousands, I thought, of posters that you had like looked at or included in the analysis. And it was over time. And so I’m curious, how did you get that data specifically?

00:26:36.86 [Stephen Follows]: Because that one fascinated me. Yeah, no, me too. It’s one of those questions that I’m so excited to answer. Like I said, I’m a fan of my own blog. I want to read these things. I just am also happy to share them. Okay, well, that one is a really good example of something that’s become not an issue that would have been an issue before, which two issues. If I tried to do that 10 years ago, I would have faced, the first issue would be, how do I take a single image and turn that into data that would be color data? I would have to go away, which I often didn’t, read Stack Overflow or talk to scientists or read up about like, how does one do that? Like, what code do I need? Can I just hack together code that already exists? Because usually, I mean, what I’m doing to gather this data is not groundbreaking. So quite a lot of people don’t know that. So I’m not going to go into that. I’m going to go into that.

00:27:17.58 [Unknown]:

00:27:17.58 [Stephen Follows]: I’m going to go into that. But quite often, someone has done that in a different context and I can just take the best of it and adapt it. So the first of the two challenges would have been, how do I do that? And second of all, how do I do that at scale? Because how do I get so many images and how do I put, I think it was, I don’t know what it was, 50, 60,000, something like that. And then how do I make, within that scale question is, how do I have data for all those things? Now, actually, it’s incredibly trivial because first of all, you can go to AI and you can have a conversation and say, hey, I want to study movie posters. I want to study color. What kind of things should I read up about? What should I consider? And for example, in that one, it’s like, well, your mind might say this poster is very orange, but when you count the number of pixels, orange may be the second or third color because it’s against a black background. So then you start realizing you can’t just count the number of pixels because what we’re carrying, what we’re looking for is saliency as a human. So then you go, well, what should I read? And yeah, exactly. So I love that process. I love going from not knowing to knowing. So the first stage would be like, okay, color. So AI solves that kind of thing. Because then you say, give me the code. There’ll be a JPEG in this form. I’ll go to this folder and I want it to generate these numbers because of this conversation has told me. And the AI can say, oh, you want this algorithm now? And all those two you might consider. And then I go and read up about them and come back and be like, yeah, we’ll do the advanced one or we’ll do this one. But I’ll put in the notes, the caveat that it has this flaw or whatever it might be. So that’s the first problem. The second problem of scale is massively solved by technology being faster. So I have a fairly fast computer. One of those I published a report a few years ago about horror films that I did sell. And that brought in some money, not very much money, but a lot of money by my standards. So I bought a nice computer with a strong graphics card and a load of hard drive space and stuff. And so I was able to leave things chunking away. And as you know, with code, if it works for one, it can work for 60,000 just as easily. And the same with data sets. Like I already have metadata on all the movies. So the main criteria I use is the IMDB ID for a movie because almost every movie is on IMDB, not alone, but almost every single one. So with that as a unique key, I can go back, to my metadata data sets and say, I know this ID means this is a comedy. So when I then have lots of posters and I know the IDs for them, I can then say, well, give me all the comedies divided by, you know, into buckets by a year. And I would, up until very recently, that would all be manual with Excel. As in, I would just, it would have 60,000 rows. I was constantly fighting Excel, like a million rows or having over many columns and like hardware issues because I was really trying to get as much as I could, but I can write and whatever. Formulas were need to build like, okay, I want all that by year, by genre. And then as for getting them, there are lots of established data sets online, whether it’s Kaggle or just searching for stuff. I find that quite often there is a kind of quasi, so there’s full data sets that have permissions and whatever. Then the other end of the spectrum, there’s private data sets and copyrighted material and things like that. And I try and avoid all that stuff, even though, as I said, sometimes I go into that space, but I’m doing it from an educational point of view. I’m deleting the data afterwards, or I’m certainly not sharing it. I’m not telling people how to do it. I feel like I get a lot of leeway because the way a scientist would, like I’m largely being ignored, which is what you want in that regard. I’m certainly not commercializing that. In the middle ground, there’s quite a few. So like with color data, it might be that I need a really big image or it might be that I can deal with a small size that is fair use that’s on the Wikipedia page. And having already got lookups for films with metadata, and then also knowing the way to get to the Wikipedia page from a previous project, it’s not that hard to just go and download all those files and knowing you’re okay, and then running it through that program. Or sometimes it is just scraping existing sites that have this data. But the hard thing as well then is to make sense of it because now I will do many deep research from AI prompts to learn about topics. But back then I would just manually read through stuff and I might be like, okay, well, first, I’m going to look at the academic literature on posters. Has somebody studied this? Have they already cracked it? But also what is the history of the movie poster? And it might be as simple as the Wikipedia page. I don’t have time to do two weeks work on everything. So I might read the history of the movie poster and discover that’s interesting. Or quite often it’s data led. I’ll see there was a big spike in the 1980s and be like, why was that? And then I have to condense that down to one question that can then be answered in an article that isn’t way too long or sprawling. And that’s usually the bit that saves me. Because I’m like, well, big data set, loads of answers, not a lot of clarity, but there is one thread, one vertical throughout all of this. I can go from beginning to end. Okay, I will write about that. And I will let all this fragmented messy stuff on the side sit for another project or another time where I have more understanding.

00:32:04.07 [Julie Hoyer]: It’s nice to hear that you start from a well thought out question though. Because a lot of times I think we, as people that work with data, we see a lot of people say, oh, there’s all this data out there. So I’ll just like, look what data is available and decide like what it can tell me. And we talk a lot about going from the opposite direction and actually starting from an interesting question and doing the research before either collecting the data yourself or grabbing what’s available. But I also think that makes someone much more attuned to if the data that is available is actually, to your point about the color, like is the data available talking about the ratio of pixels or is it representing what a person picks up on visually? Because that would possibly give very different answers to the question you’re essentially trying to ask the data. So it’s great to hear some of your process that way.

00:33:00.46 [Stephen Follows]: Yeah, I try and keep a sort of layman’s point of view. I assume everyone’s a smart layman and I don’t assume they know any knowledge about the industry or about the topic. Cause I wouldn’t, I certainly didn’t 10 years ago and I may not have 10 minutes ago. So I try and, but I try and make it so that it’s a logical set of answering. And sometimes the joy is in the answering, not in the answer. Like, you know, if I’d said to you, they’re more blue than they were before, like, okay, that doesn’t mean anything. I can’t even remember what the answer was, but the process of breaking that down or looking for the correlations within that is really fascinating. So I quite often think it is the journey, not the destination.

00:33:34.09 [Michael Helbling]: Right, Steven. So what’s in my head right now is, obviously you’ve done so much research and so much digging in and analysis. How is the movie going experience for you? What happens when you sit down in a movie theater?

00:33:49.29 [Stephen Follows]: Well, I mean, most people who get into film find that the first five years after college is ruined for them because you see the structures, all of the obvious points, Chekhov’s gun, you know, you know, something’s going to happen. And you know, the, especially non-innovative films. So most films are following a very set structure. And once you know that you see it everywhere and it kind of ruins it for a bit, and then you learn to let go. Or if you’re-

00:34:15.59 [Michael Helbling]: The Wilhelm Scream, that sort of thing.

00:34:17.55 [Stephen Follows]: Exactly. And it takes you out of the movie, right? Cause you’re like, oh yeah, there’s that thing I know. Or there’s a reference to that. And a lot of friends who work in visual effects have had visual effects movies ruined for them because they’re looking around the edge of the hair and they’re like, I can see the green screen, that’s poor work. And you’re just like, yeah, right. But after a while, you kind of have to let go of all that stuff. And my partner’s always amused when people assume that I watch good films. I don’t, I watch trash. Like I’m not a cinephile. Like I like good films. Definitely. But I think I can definitely split the studying of the film industry from the watching of it. And very rarely do they overlap in any significant sense.

00:34:58.25 [Michael Helbling]: So what’s a good movie you like? Well, wow. What’s a favorite?

00:35:08.40 [Stephen Follows]: I mean, I’m a huge fan of the Bridges of Madison County and Meryl Streep and Clint Eastwood is just extraordinary. And I- I almost don’t want to say any more about it. I want to- someone forced me to watch it. I used to, with a friend of mine many years ago, go around to his house and we used to rent a video. That’s how old it was. And we’d agreed to rent something and he got- and he saw this on TV the night before and refused to rent what we were supposed to rent and said, no, we’re going to sit down and watch this. So I watched it under protest and it’s just incredible. But also there’s this local theater near me that does throwback screenings where they show older films. And I saw Speed in the cinema. I mean, I’ve only ever seen it on video or whatever. And it’s just incredible, timeless movie. Just perfect. Like, so I think I want to get lost into a movie. I want to get- I don’t really mind if it’s emotional or fantastical. I just want to forget the real world. Sounds darker than I meant it to, but like, that’s what I want. Like, I want to get lost in it. Yeah. No, I love that. I think that makes a ton of sense.

00:36:05.75 [Tim Wilson]: Do you ever find yourself- like if you’re watching Speed in a theater, like, like some kind of not the of the moment experience, does that- do you find yourself- either in or shortly after having that trigger any of those questions? Like, do you, you know, how many movies have featured a bus that doesn’t stop?

00:36:26.90 [Stephen Follows]: Or some- I mean- That’s a good example, actually, because I want to- I haven’t finished it yet, but I’m doing a video for my YouTube channel that I’m looking at- each time I’m looking at a different actor and I’m doing one on Sandra Bullock because I think she’s great. And I’ve concluded that most of her movies are Speed, whereby she is an every woman pulled in to a situation and there is a bus. In every monom- sometimes the bus is a boyfriend, but like in every film there is like- and she- every film she’s done is Speed, basically, in a wonderful way. And because she is the every woman brought into things and I love her for it. But yeah, so I quite- yeah, I do sometimes leave movies and have ideas for stuff. Another random one, actually, I’ve got a group of friends who I’ve been watching movies with on Tuesday nights on- we stream them, you know, and I’ve got a group of friends who I’ve been watching movies with on Tuesday nights on- we stream them, on Discord just to- there’s a- there’s about 30 of us, but we’ll probably end up watching about 10 of us every night, every- every week, because some people can make it, some can’t. And we watch great films and terrible films. And we started during the beginning of the pandemic as everyone started doing things and we just haven’t stopped. And we were watching trailers to work out what to watch next week. So we picked trailers randomly and then choose it. And we watched the trailer for Unbreakable. And there is a unbreakablemovie.com or whatever domain. And someone in the group said, I wonder if that domain’s available. And I can’t remember if that one is or not, but it got me down a sidetrack where I was like, well, I think I could work that out. So I found, I went through all the data films I have, and I checked what domains they used when the film came out. This is like 99,000 movies. And then I identified which ones were, which were like proper movie domains rather than like Warnerbrothers.com slash whatever. And that’s about 30,000 and no 45,000. And I found that about 15,000 of them are available to buy. So one in three movie domains that were big part of the promotional part of the movie are currently just, you can purchase. And so I’m doing a video on that as well, but I bought one. I won’t say which one it is yet for the video, but like there are some big movies you can buy the domain for. And like, that was quite the journey because it was an idle comment someone made in a trailer, like just watching films with friends. And it just kind of, I was curious. And I just thought I’d see when I fail. I don’t mind failing in the slightest. I don’t, I just don’t want to not try. So I just kept going. And I was like, Oh, I think I learned something I didn’t need to know.

00:38:47.34 [Michael Helbling]: All right. And quick counterfactual for you on Sandra Bullock. Yes. In the blind side, I would posit she’s the bus. Nice. Yeah, there you go. So if you can work that one in.

00:39:01.07 [Tim Wilson]: You’ve mentioned it in passing a couple of times and it’s weird. This is a little meta because you’re because you’ve kind of done the filmmaking. But you started as writing these videos that you now do are. Like, incredible. Like, I look at them like these are so these cartoons like they are. They clearly reinforce your I want to be a clear explainer and a teacher. But they’re like daunting from a looking at it from a content creation, like the handful of times where I said, I just want to explain this well in a video. And I spend weeks or months because I just want to do something clearly. So can you talk about that? I mean, partly the process, partly like why? What do you shift from writing and then adding on the little kind of short film version of the writing on top of it?

00:39:54.07 [Stephen Follows]: Yeah, it’s been something that’s been really new. I think I launched in February this year and people have been telling me to do video for a long time, but I don’t really want to be on camera. Like, I just it just doesn’t appeal to me in the slightest. And it makes my screen call a little bit. I don’t mind other people doing it. I just don’t want to. If I didn’t have that part of my personality, I’d be on like one of those people on TikTok being like, hey, let me do this. I’m going to do this. Hey, let me explain this to you. But it just doesn’t work for me at all. And I also don’t want to, as we talked before, start something and not finish it. And I’m aware how big commitment is to make regular videos. So for a very long time, I didn’t do any video of any kind. And I was just doing audio stuff, guesting on podcasts and I had my own one for a little bit and then a lot of writing. And a friend of mine, Ben, who’s a really good filmmaker, was pestering me for years saying, you’ve got to do it, you’ve got to do it. And I said all this and he was like, well, why don’t we do it together and we’ll do it as a cartoon? So it’s my, we write it together based on my research, but he’s the one leading the editing work, he’s doing the editing and I’m giving notes. And then the animation is a version of me. And so that, so it came from him saying, no, it’s crazy, you’re not doing stuff. Also, there is a business opportunity in there. Not that we’re making very much money from it, but as I said before, like, I think the money follows if you’re lucky sometimes. It’s certainly not a good use of time financially. But I’m very fortunate. Although I’m very new to YouTube, I’m coming in with, I don’t know, 500 published articles already. So I know the ones that have resonated, I’ve done the work. And also it’s, the same work can be used there that can be used somewhere else. And so there’s a lot of overlap. Ben is a really, really talented filmmaker and works really hard. And, but both of us also share a slightly anarchic sense of the world. We want to take serious things silly and silly things serious. And so we try and keep the madness of a first draft. And we’re just talking about it because he’s on the movie group as well. He set the whole thing up. And so when both of us are like, yeah, I wonder what domain’s available. Let’s buy one. All right. How many can we buy? No, it’s too expensive. Let’s just buy one. How would we rank them? Oh, well, there’s a good question. And like, we try and keep that madness so that you’re watching it thinking, what is this? And because a lot of my writing is, I’m not an academic. I’ve literally never, I’m really not an academic on so many levels. But I, my writing is a bit more academic than, you know, I’m not an academic.

00:42:11.34 [Unknown]:

00:42:11.34 [Stephen Follows]: I’m not an academic. I’m not an academic. I’m not an academic. I’m a bit more academic than perhaps what I’m like in person when I give a talk. And this is the opposite. And this is the anarchic kind of, it’s YouTube. Like, I want to get it correct because I care, but it does not matter. And it matters more. Did you entertain people? Did you, you know, introduce them to a new idea? So I’m not trying to achieve anything other than just have fun for six minutes.

00:42:33.94 [Tim Wilson]: You weren’t applying, you weren’t applying those early analyses of how to make, predict that these films are going to make money. You’re like, I figured it out. Cartoon.

00:42:41.55 [Unknown]:

00:42:41.55 [Stephen Follows]: I can tell you that I have made so many poor business decisions if you were to see my life as a business thing. But, um, but that’s fine though. I mean, Ben and I really enjoy it. It’s really fun. It’s mad. It’s, but one of the things that has helped us again, to sort of use AI as an example, we could not have done this five years ago. We’re not using AI to, to come up with ideas or to write anything, but we did spend some time building a animation pipeline because in the very first ones we were doing in February, all the test ones we did last year, at the end of last year. We use, um, Adobe character animator and we create these characters and have them, the character and then the mouth moving. It was very, very, very time consuming and also animating the graphs so that the lines come on or whatever. And this was taking a very long time and we were like, wow, this is not sustainable. But this year we’ve spent a lot of time writing code through AI to do that for us. So we now have a whole pipeline where we can take something I’ve said and put it into just put it, plop it into there. So we’ve got the character animator that we’ve built. It automatically reads it. It says, okay, well this point, the character should have his happy face or his sad face. These are the mouth movements. It generates a first draft, which we go in there and change where all these tags are and then hit generate again. And it’s generated a whole, I mean, it could do an hour. It could do anything at a time. And also we use 11 labs to generate my voice, not for the final piece. The final piece is all me. But when we’re doing a rough edit early on, when we’ve just got it all in text, it’s like, is this going to sound right? Well, let’s just see how long this is. Let’s see if it makes sense. And we, so we use a lot of stuff behind the scenes to help us keep the madness of the ideas. But the execution is like having a team of 20 behind us who can just do things.

00:44:26.98 [Tim Wilson]: Wow. It just feels like that’s like the AI lesson, like the, the, the human part of keeping the madness and the madcap and the, and then like, but I don’t want to, I don’t want to record the script 15 times over. So I hate 11 labs. Exactly. Like, it’s like, you’ve, you’ve, you’ve also managed to kind of find, and I’m sure it’s still evolving sort of the sweet spot of, of how to, how to use AI, but keep the human component.

00:44:52.43 [Stephen Follows]: That is the future. That is the future. That is what, you know, I read something yesterday talking about how writers are now deliberately putting in typos and stuff like that to try and show as human. And to some degree, you know, we buy handmade soap, there’s records, there’s, you know, the flaws are, it’s not that we want it to be bad. It’s just that we, that’s the sign of humanity. I, it’s complicated as to how much that will carry on in what way, but certainly we know that the fact that this is made by two humans with a perspective is the important bit. And it’s also why we’re doing it. I don’t want to create 400 of these because I could generate them from my video, my articles. I can take all 500 of my articles. I can auto generate the voice and the animation. I could do that today. And I could also probably even use, we’ve got a lot of clips and we’ve already embedded them and we can search on them with like close up of phones. All this sort of stuff can come already. We could do all of that. And it would be soulless and empty.

00:45:38.70 [Unknown]:

00:45:38.70 [Stephen Follows]: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.

00:45:39.26 [Unknown]:

00:45:39.26 [Stephen Follows]: It would be being pointless. And why would I do it? Like, what a waste of my time. But for me working with Ben and coming up with something mad and then executing it and being like, ah, and then when someone watches it, they also go out, what was that? Like that feels a good use of my time and of the audience’s time.

00:45:54.72 [Tim Wilson]: So now that you’re kind of the guy or one of the guys who’s got built up all this, do you find, do you have people coming to you for the data that you’ve either created or like do you have to i’m trying i’m thinking i think it might be daniel paris with stat significant has like yeah he has like a a paywall to like you know access my data so that clearly kind of happened to him so are you getting people coming to you saying hey do you have anything on this

00:46:25.14 [Stephen Follows]: could i use it how could i get it okay oh yeah i mean uh 25 emails a week about that kind of stuff from the film industry like it’s quite a lot but i i almost never almost never give out that data because first of all it doesn’t come from me quite often if i’ve gathered it or scraped it from somewhere else it’s not mine so whether that’s a legal thing or a moral thing it’s just not mine it’s not really a question of me withholding um if i’ve done a truly original analysis on something or it is an open source data set then i’m much more open to it certainly if it’s for a good cause as well but i’m not i’m not going to be there to be able to support that data either so let’s say that i have taken wikipedia plot summaries and i’ve applied some kind of analysis to them and i’ve got a data set for it uh you can’t do that without a data set so i’m not going to be able to support that data either that’s fine but if i then like um i did a lot of that stuff with embedding uh uh in text embedding if i then have to explain to somebody what this is what model i used how it worked it’s a huge amount of servicing for that bit of data and then maybe they want it slightly different i can’t i can’t service that i just don’t have the time for that so i do try and help where i can but quite often uh very very rarely can i actually help the core way they want help for as well as the fact that most of the people within the industry that come to me want it as a silver bullet as a panacea to prove that the thing they already want to make is going to make money and it’s not going to and i’m not going to be able to prove it so i’ve got very good at saying no like i 99 of the time people email me for something i have to kindly say that’s not possible or i’m not doing it um and that’s fine like that i’m very happy for people to ask don’t mind saying no but i think i have thought a long time about linking to other people’s data like i have a huge data set from all the public bodies around the world and all of the data they have i’m like it’s i don’t know three or four hundred gigabytes and i could put that back online but again it’s not my stuff even public bodies like i’d be better saying hey go to the korean film institute and then maybe they change what their website is or how it works and i just can’t support that so i just say i can’t or individually i’ll say okay go here and have a look and go get it yourself so so maybe

00:48:27.21 [Tim Wilson]: that’s the not that i know where michael’s gonna be like we’re heading towards a wrap but the the and i don’t want to make this a downer but i’m sort of mentioned it briefly earlier can you talk a little bit about kind of the ai impact on the whole tale of the numbers and we’ll link to your post about it but to me that was kind of a oh like i had not thought through how ai could literally basically crush a data store i have a

00:48:56.01 [Stephen Follows]: very complicated relationship with that ai i use a huge i got four claude max accounts like and i really use every credit i am really using it at the same time i’m deeply worried about the future and where we’re going and what the effects of this are and i was very lucky i got an early access to gpt3 when in 2022 something like that um and i used it to do an art project with a uh an astrophysicist in sweden uh we managed to convince a hollywood producer to give us money to go and write a script and so they paid us the same amount they normally have pay a writer and we developed that in 2022 before chat gbt so i’ve been around that kind of stuff for a while and i and if there’s something we don’t know about this let me know in the comments because i would love to hear from you if you’re interested in such things as well i’ll see you next week next week And I feel a lot like Cassandra because I keep saying how much it’s changing things. I’m not saying automatically better or worse, but like the world everyone’s walking around. My partner’s not very technophobe. She wouldn’t say she’s technophobe or a Luddite, but she’s just like, well, I didn’t need it. And I feel like it’s two realities of like how much this is fundamentally transforming so much. At the same time, you go out and you buy an ice cream and everything looks the same. And maybe it will be the same in 10 years. I don’t know. But it’s the biggest change I think we’ve been through. It’s certainly at this speed as a race, as a human race. Right. And so I’m interested in telling that story. And it’s very hard to not just be shouting people. So this particular story about the numbers.com was a really good example that fell in my lap to be able to say, look, here’s just one example. So a bit of background. The numbers.com is one of the foremost box office tracking websites. And it used to be in competition with Box Office Mojo. But Box Office Mojo was bought. It was owned by IMDB. And then just before the pandemic, they put it behind, some of it behind the paywall. They sort of made the inshittification of that because it was now owned by Amazon. So the numbers became preeminent. And I’ve known Bruce Nash, who runs the numbers, probably as long as I’ve been in this stuff, because I probably went to him first asking for data. He’s British, but he’s lived in San Francisco most of his adult life. And every time I go to the West Coast, I go and spend some time with him and his lovely wife. And so I got to know him really, really well. Like we’re good friends. And he does a few things. He does market analysis for filmmakers. They sell their data in bulk to studios and to hedge funds and other. And then they also have this website called the numbers.com, which has public box office data going back years. Lots of really cool tools and things like that. In March this year, the site just went down and just wasn’t publicly available. And I don’t talk to Bruce all the time, but I reached out and said, is everything all right? And he kind of went dealing with some stuff. Talk to you later. All right, fine. And then. It was a good couple of months. I knew he was very busy because it was something we were supposed to do and he kept pushing it off. And so in the end, I did have a Zoom with him and I was like, hey, what’s going on? And we caught up socially. And then he was like, right, I need to tell you the story. And he told me what happened. And at the end of it, I said, look, this is really self-serving, but can I write about this? Because obviously we’re just friends. He told me off the record. But because it fits into this wider narrative, I think people should know. How do you feel about that? And he thought about it and was like, yeah, actually, he’s a big. Yeah. Advocate for free speech and for open data. And he actually took me to the Internet Archive. They have a church in San Francisco where the server is a little blinky light. He took me there years ago. So I know he’s this is part of his belief as well. So I went with him to write this long article about what happened. And there’s sort of two things that really affected him. One is the massive increase in AI bot traffic. So there was always search engine traffic that would slow down websites or at least cause problems. For servers. But the deal was that search engines would cruel this information and then send you traffic because they’d be like you’d search for something and then they’d deliver the page. And it was manageable. But about a year or so ago, they started most website hosts, certainly data rich sites, started to see a massive increase in the number of bots to the point to which I think there’s some study that I managed to find numbers for that’s in the article that talks about how many page views the bots would go on to compared to how many humans they send you. And for search engines, it was something like every 10 views. I can’t remember the ratio. It’s very, very low number. For Anthropic, it was 32,000 sites visits, site visits from an AI bot for each one human being they actually sent you as traffic. So it’s an overwhelming volume of traffic, you know, just swamping these sites. And so that the numbers were suffering from that. They did some clever stuff. You can make your pages lighter. You can do stuff with robots.txt. But also they put. A little thing in there that said, if you’re an LLM, you can license our data this way. And that helped reduce the traffic. But then this other AI thing happened, which is that Polymarket, everyone’s favorite slash worst prediction market, has markets where you can bet on the outcome of box office. And they use the numbers as the answer. There’s the kind of like that’s what will resolve this contract. And so somebody or some people, I’m sure it wasn’t just one, started using tools off the shelf. Like, I don’t know if it was Claude or Chad GPT, but it’s not hard to imagine. It’s one of those type things to search for vulnerabilities. And this is a 30 year old website with over 150,000 legacy pages. And it found some and it kept hammering it and hammering it. And I’m not at liberty to give all the information away, but it looks like and certainly my reading of it is that somebody with a basic AI tool broke into his website, took it over to try and front run the numbers to try and see before it gets published. Oh, just the last minute, you can see that film is doing better than this. Or maybe they published them on Mondays, but they’re putting them in on each day, Friday, Saturday and Sunday. I don’t know what they thought would be the case. And so it took down the site and they can’t turn the server back on. And Bruce is now sitting there thinking he’s got a basic version of the site back up. The rest of his business is surviving because it’s the other types of business. But he’s sitting there thinking and he was asking me about what do I do with a website in 2026? Do we even have a website? If I’m going to have another website, is it going to get taken down again? So what’s the point? Like, I can’t even get human traffic. So, yeah, a real sign of the times. But it’s a long read and it’s worth it because and it’s got loads of Bruce’s quotes throughout it because he was very generous with his time. If you’re interested in thinking about how the world is changing and how AI is affecting it in ways you may not think, it’s really worth reading. But give yourself 10 minutes afterwards before you interact with people because you’ll be sad.

00:55:28.42 [Tim Wilson]: Well, I mean, on the on the business side, there’s all this like, how do we show up? You know, all these companies saying. What do we need to do to show up? You know, the whole AO and GEO. Like, I want to show up in those results. Like, so it’s like, oh, it’s like, go counterpoint this. Yeah. Wow. I did head us towards a downer. All right. Good job. Yeah. Good job.

00:55:54.26 [Michael Helbling]: Yeah. Overall, this has been a fascinating conversation. And thank you, Stephen, so much for coming on. And talking about it. And I think it’s sort of universal, like a love for movies and looking at sort of the stories and the data beneath them. One thing we like to do on the show is go around and share a last call, something that might be of interest to our listeners. And Stephen, you’re our guest. Do you have a last call you’d like to share?

00:56:23.03 [Stephen Follows]: Yeah, I got a website, which some of you will know inside out and some of you may never have heard of. I hope you’ve never heard of it because you’re about to lose three or four hours of pure joy. Go to pudding.cool. P-U-D-D-I-N-G. Please say, yeah. P-U-D-D-I-N-G.C-O-O-L. They are incredible data projects. They make you think. There’s a new one up there about a load of menus from New York and around America. And what can the hash of menus tell us about America? You’re going to spend so much time on it. I love it.

00:56:55.44 [Tim Wilson]: It’s nice. Very nice. Remember when they broke down like Ali Wong’s first special or something? I mean, it’s just bonkers. It is. Boy, that is amazing. There was. In the vein of this discussion. Yeah.

00:57:06.50 [Michael Helbling]: If there remained any doubt, Stephen, that you were not one of us, you could dispel all of that. No, there was no doubt. Awesome. Very cool. All right, Julie, what about you? What’s your last call?

00:57:19.32 [Julie Hoyer]: Mine is a YouTube video from David Epstein. Actually, it was his one of his newsletters recently. But I loved it because it was sports plus science plus, you know, some fun numbers and stats. And like, that’s right up my alley. So it was why messaging? I see barely runs. And he just like goes through kind of this whole broad analysis and deep dives into psychology. And it is just I loved it. It was so good. So highly recommend. Very cool.

00:57:48.03 [Tim Wilson]: I read that. And then I watched one of the games. I was like, oh, my God. I mean, I follow more closely or like aware of that. And I was like, oh, oh, my God. Like, he really that’s wild.

00:57:58.90 [Julie Hoyer]: Yeah, I know. I was shocked. It was it was great. Full of fun tidbits. Yeah.

00:58:05.34 [Michael Helbling]: All right, Tim. What about you? What’s your last call?

00:58:09.08 [Tim Wilson]: I’m going to go with Dave. I’ve I’ve had Eric Sandisham articles before, but he wrote one called Three Kinds of AI. And it’s short. And he breaks down like predictive, generative, energetic, energetic, a little messier and talks about kind of gives a definition to me, like very, very useful because people say I. And I feel like we talk past each other. And he has. Some thoughts on like the ease of measuring and getting achieving ROI from each different type. But it’s his articles tend to be short and they tend to be kind of brilliant. And that one I have not stopped thinking about since he published it. What about you, Michael?

00:58:53.32 [Michael Helbling]: Yeah, it’s interesting. I sort of involves this conversation a little bit. I was reminded of a very favorite one of my favorite authors, Douglas Adams. It is. It’s a book, Life, the Universe and Everything has this definition of learning how to take on the knack of flying, which is learning how to throw yourself at the ground and miss. So it’s sort of like a repetitive process where you throw yourself at the ground and then try to distract yourself before you hit. And it sort of reminds me of the advice of like, yeah, how do you succeed in some of these sort of things? It’s sort of like that. And it’s sort of. It’s mind twisting to be like, OK, don’t try to replicate what I did, but just sort of like throw yourself at the ground and then miss. So anyways, and also just read, if you haven’t, The Hitchhiker’s Guide to the Galaxy Trilogy in five parts. I don’t know if there are books I’ve laughed harder reading than those. So anyway, so that’s my last call.

00:59:57.23 [Tim Wilson]: On the list of people we lost too soon because that guy’s brain.

01:00:02.41 [Michael Helbling]: It was different. Very different. All right. Well, it’s been great. And I’m sure as you’ve listened, maybe you’ve got questions or maybe you want to reach out to us. We would love to hear from you. The best way to do that is to reach out to us on our LinkedIn group or on the measure Slack chat or through email at contact at analytics hour dot IO. You can follow Stephen’s work at Stephen follows dot com, where there are articles upon articles for any interest you might have on exploring data. And movies. It’s it’s almost endless. So it’s like so hard to know where to start. You’re like, just find an article and dig in. You’ll be there for quite some time. It’s excellent. And Stephen, once again, thank you so much for coming on. It’s been such a pleasure. I think hearing people explain what they’re passionate about kind of ignites your own passion in a way is sort of inspiring and encouraging. And like, I’m not going to. Dig into film like you have, but like it kind of like reinvigorates me for the things I’m excited about. So thank you so much for sharing that.

01:01:10.27 [Stephen Follows]: Such a pleasure. And thank you for destroying my idea of how long an hour is because that flew by and it does not feel like it goes quick.

01:01:18.94 [Michael Helbling]: All right. And of course, please, as you’re listening on whatever platform, leave us a rating and review. And you can also reach out to us if you’d like a sticker for your water bottle or laptop. And you can do that on analytics hour dot IO. So.

01:01:33.03 [Unknown]:

01:01:33.09 [Michael Helbling]: Yeah, lots of ways to engage. And I think I speak for both of my co-hosts, Julie and Tim, when I say no matter what the box office is going to be on your next major dashboard report, keep analyzing.

01:01:49.07 [Announcer]: Thanks for listening. Let’s keep the conversation going with your comments, suggestions and questions on Twitter at at analytics hour on the web at analytics hour dot IO. Our LinkedIn group and the measured chat Slack group. Music for the podcast by Josh Crowhurst. Oh, smart guys want to fit in. So they made up a term called analytics. Analytics don’t work.

01:02:13.79 [Charles Barkley]: Do the analytics say go for it no matter who’s going for it? So if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition.

01:02:26.46 [Michael Helbling]: But it’s just because I’ve seen it and I thought it was an incredible piece of film. No, but I’m. Going to go back and see it. I’m I wasn’t sure initially if it was worth it. And then I saw it and I was like, now I need to go back and see it at IMAX. Yeah, I will go see it in IMAX. And then lastly, Stephen, we like to send a small gift to say thank you for coming on the show. If you have the ability right below this thing, this is mailing address in the document. There’s a little box. If you just could type a good mailing address for you, then we can send that out to you. That’s very kind. Oh, yeah. There I see. Say thanks. Yeah, that’s. And that’s we just want to say, you know, thank you for taking the time. It’s a.

01:03:07.32 [Stephen Follows]: Well, I hope you send it after, though, it’s in case I do it terribly. And then you can just send an empty box to me. We have we have two different gifts.

01:03:18.76 [Michael Helbling]: And you’ll never know.

01:03:21.69 [Tim Wilson]: Actually, I’ve never shared. I I was this close because I kind of keep the the warehouse keeps just boxes and they’re already sort of pre and then stuff gets just a little addition. And I. I was dangerously close. I. I was dangerously close to actually shipping an empty box accidentally once.

01:03:36.82 [Julie Hoyer]: Oh, my God. Because I just kind of.

01:03:37.90 [Tim Wilson]: Such a bold move.

01:03:38.98 [Michael Helbling]: Oh, well, thanks for coming on the show. Yeah, we giving you back what you gave us. Nothing. But. Yeah. No, no, no, no, no, no.

01:04:02.15 [Tim Wilson]: Rock. Red flag and Chekhov’s gun.